import gradio as gr
import random
import os
from deep_translator import GoogleTranslator
from huggingface_hub import InferenceClient

# Project by Nymbo

MODEL_ID = "black-forest-labs/FLUX.1-dev"
API_TOKEN = os.getenv("HF_READ_TOKEN")
timeout = 100

# Function to query the API and return the generated image
def query(prompt, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024, oauth_token: gr.OAuthToken | None = None):
    if prompt == "" or prompt is None:
        return None

    key = random.randint(0, 999)

    # Translate the prompt from Russian to English if necessary
    if any('Ѐ' <= ch <= 'ӿ' for ch in prompt):
        try:
            prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
        except Exception as e:
            print(f"Translation skipped: {e}")
    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')

    # Add some extra flair to the prompt
    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mGeneration {key}:\033[0m {prompt}')

    seed = int(seed) if seed != -1 else random.randint(1, 1000000000)
    token = oauth_token.token if oauth_token is not None else API_TOKEN
    print("token source:", "visitor" if oauth_token is not None else "space secret")
    if not token:
        raise gr.Error("Please sign in with Hugging Face to generate images.")
    client = InferenceClient(token=token, timeout=timeout)
    try:
        image = client.text_to_image(
            prompt,
            model=MODEL_ID,
            negative_prompt=is_negative or None,
            num_inference_steps=int(steps),
            guidance_scale=cfg_scale,
            width=int(width),
            height=int(height),
            seed=seed,
        )
    except Exception as e:
        print(f"Error: Failed to get image: {e}")
        raise gr.Error(f"Image generation failed: {e}")
    print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
    return image

# CSS to style the app
css = """
#app-container {
    max-width: 800px;
    margin-left: auto;
    margin-right: auto;
}
"""

# Build the Gradio UI with Blocks
with gr.Blocks() as app:
    # Add a title to the app
    gr.HTML("<center><h1>FLUX.1-Dev</h1></center>")
    gr.LoginButton()
    
    # Container for all the UI elements
    with gr.Column(elem_id="app-container"):
        # Add a text input for the main prompt
        with gr.Row():
            with gr.Column(elem_id="prompt-container"):
                with gr.Row():
                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
                
                # Accordion for advanced settings
                with gr.Row():
                    with gr.Accordion("Advanced Settings", open=False):
                        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
                        with gr.Row():
                            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32)
                            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32)
                        steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
                        cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
                        strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random
                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])

        # Add a button to trigger the image generation
        with gr.Row():
            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
        
        # Image output area to display the generated image
        with gr.Row():
            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
        
        # Bind the button to the query function with the added width and height inputs
        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)

# Launch the Gradio app
app.launch(theme='Nymbo/Nymbo_Theme', css=css)